Academy

Efficiency metrics — yards per carry, yards per route, touchdown rate — are dominated by variance at the sample sizes a fantasy season provides. A running back's yards per carry in the first eight games tells you remarkably little about his next eight. His carries per game tells you a great deal.

This is why the factor library weights usage heavily and efficiency lightly, and why the single most valuable piece of news in fantasy football is a depth-chart change rather than a highlight.

The practical rule: when you are choosing between a player who is efficient in a small role and one who is mediocre in a large one, take the large role almost every time. The role is the asset.

Touchdown rate per opportunity regresses hard toward positional means. That part is true and well documented. The mistake is assuming the market has not noticed.

By the time a player is publicly described as 'due for regression', his ADP and his projections usually reflect it. The model shrinks scoring rate toward a league prior with a weight of 40 opportunities — strong enough that a two-game hot streak barely moves the projection.

The real edge is in the opposite direction: finding players whose *volume* is about to change, which no amount of regression math will surface.

A point projection collapses a distribution into its median and discards the information you actually need. A 14-point running back with a 6-point floor and a 22-point ceiling and a 14-point receiver with a 0-point floor and a 41-point ceiling are not interchangeable, and no single number can tell them apart.

Which one you want depends entirely on the objective. Beating a median (cash games, head-to-head) rewards floors. Finishing first out of 100,000 rewards ceilings and nothing else.

This is why every projection on this site ships with a floor, a ceiling and a standard deviation, and why the Monte Carlo engine exists at all.

A quarterback and his top receiver share the same passing yards — their weekly outcomes correlate around +0.46. Two backs in the same backfield split one set of carries, correlating around −0.38. Two players in the same shootout both benefit when the game stays close.

Ignore this and your simulated lineup distribution is far too narrow: you will systematically understate both how good and how bad your week can be. Since tournaments only pay the right tail, understating the right tail is the expensive error.

Stacking a quarterback with his receiver does not raise your median much. It substantially fattens your 95th percentile. That is the entire argument for it.

Leverage is ceiling-hit rate divided by projected ownership. A player who spikes 18% of the time at 4% ownership returns more tournament equity per roster slot than one who spikes 26% of the time at 40% ownership — even though the second player is unambiguously better.

The corollary is uncomfortable: the optimal tournament lineup is usually not the highest-projected lineup, and it will feel wrong when you submit it.

The measure that keeps you honest is expected duplicates. Multiply your players' ownerships together, scale to the field size, and you get roughly how many identical entries you are competing against. If that number is in the hundreds, you cannot win outright regardless of how many points you score.

Leak one same-week statistic into your features and R² jumps to 0.9. It is trivially easy to do — a season-long average that includes the target week, a 'current' depth chart scraped after the game, an injury designation that resolved on Sunday morning.

The defense is walk-forward validation with strict inequalities: features come only from weeks strictly before the target, weights are refit each week, and the test week is never seen during fitting.

The second defense is a hard baseline. If a model cannot beat a three-game exponentially weighted average, it is not a model, it is a moving average with extra steps. That comparison is printed on every row of the Backtest Desk.